scholarly journals Measurements of turbulence transfer in the near-surface layer over the Antarctic sea-ice surface from April through November in 2016

2020 ◽  
Vol 61 (82) ◽  
pp. 12-23
Author(s):  
Changwei Liu ◽  
Zhiqiu Gao ◽  
Qinghua Yang ◽  
Bo Han ◽  
Hong Wang ◽  
...  

AbstractThe surface energy budget over the Antarctic sea ice from 8 April 2016 through 26 November 2016 are presented. From April to October, Sensible heat flux (SH) and subsurface conductive heat flux (G) were the heat source of surface while latent heat flux (LE) and net radiation flux (Rn) were the heat sink of surface. Our results showed larger downward SH (due to the warmer air in our site) and upward LE (due to the drier air and higher wind speed in our site) compared with SHEBA data. However, the values of SH in N-ICE2015 campaign, which located at a zone with stronger winds and more advection of heat in the Arctic, were comparable to our results under clear skies. The values of aerodynamic roughness length (z0m) and scalar roughness length for temperature (z0h), being 1.9 × 10−3 m and 3.7 × 10−5 m, were suggested in this study. It is found that snow melting might increase z0m. Our results also indicate that the value of log(z0h/z0m) was related to the stability of stratification. In addition, several representative parameterization schemes for z0h have been tested and a couple of schemes were found to make a better performance.

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Nicola Scafetta ◽  
Adriano Mazzarella

Here we study the Arctic and Antarctic sea-ice area records provided by the National Snow and Ice Data Center (NSIDC). These records reveal an opposite climatic behavior: since 1978 the Arctic sea-ice area index decreased, that is, the region has warmed, while the Antarctic sea-ice area index increased, that is, the region has cooled. During the last 7 years the Arctic sea-ice area has stabilized while the Antarctic sea-ice area has increased at a rate significantly higher than during the previous decades; that is, the sea-ice area of both regions has experienced a positive acceleration. This result is quite robust because it is confirmed by alternative temperature climate indices of the same regions. We also found that a significant 4-5-year natural oscillation characterizes the climate of these sea-ice polar areas. On the contrary, we found that the CMIP5 general circulation models have predicted significant warming in both polar sea regions and failed to reproduce the strong 4-5-year oscillation. Because the CMIP5 GCM simulations are inconsistent with the observations, we suggest that important natural mechanisms of climate change are missing in the models.


2010 ◽  
Vol 10 (8) ◽  
pp. 18807-18878 ◽  
Author(s):  
S. J. Doherty ◽  
S. G. Warren ◽  
T. C. Grenfell ◽  
A. D. Clarke ◽  
R. E. Brandt

Abstract. Absorption of radiation by ice is extremely weak at visible and near-ultraviolet wavelengths, so small amounts of light-absorbing impurities in snow can dominate the absorption of solar radiation at these wavelengths, reducing the albedo relative to that of pure snow, contributing to the surface energy budget and leading to earlier snowmelt. In this study Arctic snow is surveyed for its content of light-absorbing impurities, expanding and updating the 1983–1984 survey of Clarke and Noone. Samples were collected in Alaska, Canada, Greenland, Svalbard, Norway, Russia, and the Arctic Ocean during 2005–2009, on tundra, glaciers, ice caps, sea ice, frozen lakes, and in boreal forests. Snow was collected mostly in spring, when the entire winter snowpack is accessible for sampling. Sampling was carried out in summer on the Greenland ice sheet and on the Arctic Ocean, of melting glacier snow and sea ice as well as cold snow. About 1200 snow samples have been analyzed for this study. The snow is melted and filtered; the filters are analyzed in a specially designed spectrophotometer system to infer the concentration of black carbon (BC), the fraction of absorption due to non-BC light-absorbing constituents and the absorption Ångstrom exponent of all particles. The reduction of snow albedo is primarily due to BC, but other impurities, principally brown (organic) carbon, are typically responsible for ~40% of the visible and ultraviolet absorption. The meltwater from selected snow samples was saved for chemical analysis to identify sources of the impurities. Median BC amounts in surface snow are as follows (nanograms of carbon per gram of snow): Greenland 3, Arctic Ocean snow 7, melting sea ice 8, Arctic Canada 8, Subarctic Canada 14, Svalbard 13, Northern Norway 21, Western Arctic Russia 26, Northeastern Siberia 17. Concentrations are more variable in the European Arctic than in Arctic Canada or the Arctic Ocean, probably because of the proximity to BC sources. Individual samples of falling snow were collected on Svalbard, documenting the springtime decline of BC from March through May. Absorption Ångstrom exponents are 1.5–1.7 in Norway, Svalbard, and Western Russia, 2.1–2.3 elsewhere in the Arctic, and 2.5 in Greenland. Correspondingly, the estimated contribution to absorption by non-BC constituents in these regions is ~25%, 40%, and 50%, respectively. It has been hypothesized that when the snow surface layer melts some of the BC is left at the top of the snowpack rather than being carried away in meltwater. This process was observed in a few locations and would cause a positive feedback on snowmelt. The BC content of the Arctic atmosphere has declined markedly since 1989, according to the continuous measurements of near-surface air at Alert (Canada), Barrow (Alaska), and Ny-Ålesund (Svalbard). Correspondingly, the new BC concentrations for Arctic snow are somewhat lower than those reported by Clarke and Noone for 1983–1984, but because of methodological differences it is not clear that the differences are significant.


2021 ◽  
Author(s):  
Ryan Fogt ◽  
Amanda Sleinkofer ◽  
Marilyn Raphael ◽  
Mark Handcock

Abstract In stark contrast to the Arctic, there have been statistically significant positive trends in total Antarctic sea ice extent since 1979, despite a sudden decline in sea ice in 2016(1–5) and increasing greenhouse gas concentrations. Attributing Antarctic sea ice trends is complicated by the fact that most coupled climate models show negative trends in sea ice extent since 1979, opposite of that observed(6–8). Additionally, the short record of sea ice extent (beginning in 1979), coupled with the high degree of interannual variability, make the record too short to fully understand the historical context of these recent changes(9). Here we show, using new robust observation-based reconstructions, that 1) these observed recent increases in Antarctic sea ice extent are unique in the context of the 20th century and 2) the observed trends are juxtaposed against statistically significant decreases in sea ice extent throughout much of the early and middle 20th century. These reconstructions are the first to provide reliable estimates of total sea ice extent surrounding the continent; previous proxy-based reconstructions are limited(10). Importantly, the reconstructions continue to show the high degree of interannual Antarctic sea ice extent variability that is marked with frequent sudden changes, such as observed in 2016, which stress the importance of a longer historical context when assessing and attributing observed trends in Antarctic climate(9). Our reconstructions are skillful enough to be used in climate models to allow better understanding of the interconnected nature of the Antarctic climate system and to improve predictions of the future state of Antarctic climate.


2018 ◽  
Vol 31 (23) ◽  
pp. 9771-9786 ◽  
Author(s):  
Ana C. Ordoñez ◽  
Cecilia M. Bitz ◽  
Edward Blanchard-Wrigglesworth

Sea ice predictability is a rapidly growing area of research, with most studies focusing on the Arctic. This study offers new insights by comparing predictability between the Arctic and Antarctic sea ice anomalies, focusing on the effects of regional differences in ice thickness and ocean dynamics. Predictability in simulated regional sea ice area and volume is investigated in long control runs of an Earth system model. Sea ice area predictability in the Arctic agrees with results from other studies, with features of decaying initial persistence and reemergence because of ocean mixed layer processes and memory in thick ice. In pan-Arctic averages, sea ice volume and the area covered by thick ice are the best predictors of September area for lead times greater than 2 months. In the Antarctic, area is generally the best predictor of future area for all times of year. Predictability of area in summer differs between the hemispheres because of unique aspects of the coupling between area and volume. Generally, ice volume only adds to the predictability of summer sea ice area in the Arctic. Predictability patterns vary greatly among different regions of the Arctic but share similar seasonality among regions of the Antarctic. Interactive ocean dynamics influence anomaly reemergence differently in the Antarctic than the Arctic, both for the total and regional area. In the Antarctic, ocean dynamics generally decrease the persistence of area anomalies, reducing predictability. In the Arctic, the presence of ocean dynamics improves ice area predictability, mainly through mixed layer depth variability.


2020 ◽  
Author(s):  
Linette Boisvert ◽  
Joseph MacGregor ◽  
Brooke Medley ◽  
Nathan Kurtz ◽  
Ron Kwok ◽  
...  

<p>NASA’s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by ‘bridging the gap’ between NASA’s ICESat (2003–2009) and ICESat-2 (launched September 2018) satellite missions. OIB’s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community’s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA’s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane’s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.</p><p> </p>


2018 ◽  
Vol 31 (16) ◽  
pp. 6353-6370 ◽  
Author(s):  
Mark England ◽  
Lorenzo Polvani ◽  
Lantao Sun

Abstract Models project that Antarctic sea ice area will decline considerably by the end of this century, but the consequences remain largely unexplored. Here, the atmospheric response to future sea ice loss in the Antarctic is investigated, and contrasted to the Arctic case, using the Community Earth Systems Model (CESM) Whole Atmosphere Coupled Climate Model (WACCM). Time-slice model runs with historic sea ice concentrations are compared to runs with future concentrations, from the late twenty-first century, in each hemisphere separately. As for the Arctic, results indicate that Antarctic sea ice loss will act to shift the tropospheric jet equatorward, an internal negative feedback to the poleward shift associated with increased greenhouse gases. Also, the tropospheric response to Antarctic sea ice loss is found to be somewhat weaker, more vertically confined, and less seasonally varying than in the case of Arctic sea ice loss. The stratospheric response to Antarctic sea ice loss is relatively weak compared to the Arctic case, although it is here demonstrated that the latter is still small relative to internal variability. In contrast to the Arctic case, the response of the ozone layer is found to be positive (up to 5 Dobson units): interestingly, it is present in all seasons except austral spring. Finally, while the response of surface temperature and precipitation is limited to the southern high latitudes, it is nonetheless unable to impact the interior of the Antarctic continent, suggesting a minor role of sea ice loss on recent Antarctic temperature trends.


2014 ◽  
Vol 8 (4) ◽  
pp. 1289-1296 ◽  
Author(s):  
I. Eisenman ◽  
W. N. Meier ◽  
J. R. Norris

Abstract. Recent estimates indicate that the Antarctic sea ice cover is expanding at a statistically significant rate with a magnitude one-third as large as the rapid rate of sea ice retreat in the Arctic. However, during the mid-2000s, with several fewer years in the observational record, the trend in Antarctic sea ice extent was reported to be considerably smaller and statistically indistinguishable from zero. Here, we show that much of the increase in the reported trend occurred due to the previously undocumented effect of a change in the way the satellite sea ice observations are processed for the widely used Bootstrap algorithm data set, rather than a physical increase in the rate of ice advance. Specifically, we find that a change in the intercalibration across a 1991 sensor transition when the data set was reprocessed in 2007 caused a substantial change in the long-term trend. Although our analysis does not definitively identify whether this change introduced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid-2000s, and numerous studies that have relied on these observations should be reexamined to determine the sensitivity of their results to this change in the data set. Furthermore, a number of recent studies have investigated physical mechanisms for the observed expansion of the Antarctic sea ice cover. The results of this analysis raise the possibility that much of this expansion may be a spurious artifact of an error in the processing of the satellite observations.


2022 ◽  
Author(s):  
Christian Melsheimer ◽  
Gunnar Spreen ◽  
Yufang Ye ◽  
Mohammed Shokr

Abstract. Polar sea ice is one of the Earth’s climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013–2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.


2021 ◽  
Author(s):  
Marika M. Holland ◽  
David Clemens-Sewall ◽  
Laura Landrum ◽  
Bonnie Light ◽  
Donald Perovich ◽  
...  

Abstract. We assess the influence of snow on sea ice in experiments using the Community Earth System Model, version 2 for a pre-industrial and a 2xCO2 climate state. In the pre-industrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both sea ice growth and sea ice melt due to the snow’s impact on conductive heat transfer and albedo, respectively. This leads to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow-ice formation and is balanced by larger basal ice melt. In the warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the pre-industrial climate. Whereas, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the pre-industrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled earth system models, due to its impact on a number of competing processes and feedbacks.


2021 ◽  
Author(s):  
Isobel R. Lawrence ◽  
Andy Ridout ◽  
Andrew Shepherd

<p>Snow on Antarctic sea ice is an important yet poorly resolved component of the global climate system. Whilst much attention over the past few years has been dedicated to producing reanalysis-forced models of snow on sea ice in the Arctic, none currently exist for the Southern Hemisphere. Here we present a Lagrangian-framework model of snow depth on Antarctic sea ice, in which “parcels” of ice accumulate snow as they drift around the ocean according to daily ice motion vectors. Snow accumulates from two sources; (i) snowfall from ERA5 atmospheric reanalysis and (ii) snow blown off the Antarctic continent, which we estimate using the RACMO2 ice sheet mass balance model. Ice parcels lose snow via wind-redistribution into leads and through snow-ice formation. We validate our dynamic snow product against ship-based measurements from the ASPeCT data archive, and we compare our long-term climatology against estimates derived from passive microwave (AMSR-E/2) satellites. Finally, we assess regional trends in snow depth over the past four decades and investigate whether these are driven by changes in snowfall or divergence/convergence of the Antarctic sea ice pack. </p>


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